TIME-DEPENDENT PROBABILISTIC SEISMIC HAZARD ASSESSMENT

GYANENDRA PRAKASH RAHUL

STRUCTURAL DYNAMICS

13526015

DEPARTMENT OF EARTHQUAKE ENGINEERING

OBJECTIVE

• INTRODUCTION

• TIME-DEPENDENT PSHA

• BASIC CONCEPTS

• METHODOLOGY

• A CASE STUDY ON LOWER RHINE EMBAYMENT, GERMANY

• INTRODUCTION

• METHODOLOGY

• SHORT TERM CLUSTERING

• APPLICATIONS AND RESULTS

• CONCLUSIONS

• TIME-DEPENDENT PSHA OF NORTH-EAST INDIA

INTRODUCTION

• Earthquake sequences seem to be globally continuous over time.

• Earthquakes spark other earthquakes as tectonic stresses move around in the fault

network.

• This explains the complex physical progression of earthquakes.

• Despite knowing the time-dependence of earthquake occurrences, it is mostly

uncared for.

• It has been found that on average the theory of uncorrelated random earthquake

activity underestimates the hazard by 5–10 per cent.

TIME-DEPENDENT PSHA

• In the very common approach to seismic hazard assessment, the temporal

behaviour of earthquake is assumed to be based upon Poisson model.

• On the other hand recent studies show unambiguous perturbations from a

Poissonian occurrence in seismic catalogues, and the presence of cluster activity

after a mainshock and its influence is worldwide accepted.

BASIC CONCEPTS

METHODOLOGY

• Developed by USGS, it is a simple method of prediction of the rate of

aftershocks based upon statistical parameters of aftershock distribution.

• Main drawback of this model is that it does not predict time, magnitude and

location of aftershocks.

• Used to monitor aftershock activities in California, also for daily seismicity

forecasts in Italy.

1. SHORT-TERM EARTHQUAKE PROBABILITY (STEP) MODEL

2. EPIDEMIC TYPE AFTERSHOCK SEQUENCE (ETAS) MODEL

• It is a multigenerational model in which aftershocks from one earthquake causes

their own aftershock sequences because of multiple generations of earthquakes.

• It forecasts magnitude, space and time dependence of observed seismicity above

some threshold magnitude.

• The major advantage of this model is that discrimination of events is not required

and consideration of seismic dependence of past events to present seismicity.

• Poisson process is used for background event modelling and succession of

aftershocks is described by modified Omori-Utsu law, which is given as

where, t = time since occurrence of the shock

K = aftershock productivity which depends upon lower bound of

magnitude of aftershocks counted in v (t)

p = power law exponent

c = artefact related to difficulties in detecting events shortly after

mainshock

𝑣(𝑡) = 𝐾 𝑡 + 𝑐 −𝑝

CASE STUDIES

• It is a low seismicity region in North-

Western Germany.

• The two sites under study was Cologne and

Aachen.

• Seismicity data came from Leydecker

catalogue, 2005, which was complete for

ML ≥ 2.0 since 1974.

1. LOWER RHINE EMBAYMENT, GERMANY

1.1 INTRODUCTION

• Short term clustering was modelled through ETAS and via Monte Carlo

technique it was applied on timescales with 50 year of exposure time.

1.2 METHODOLOGY

• 20,000 synthetic catalogues of time duration 50 years were generated for hazard

assessment.

• The probability of non-exceedance of a level A* ground motion at a particular

location in time t was computed by counting the intervals in which A* didn't

occur

P(A* ; t) = probability that A* is exceeded at least once in time period t

N = number of catalogues of time duration t, H = Heaviside function

Amax,i = maximum ground motion value occurred at a location during the ith catalogue

𝑃(𝐴 ∗; 𝑡) = lim𝑁→∞

1

𝑁

𝑖=1

𝑁

𝐻(𝐴 ∗ −𝐴max,𝑖

1.3 SHORT TERM CLUSTERING

• Aftershock activity was included in the analysis by ETAS modelling, which is a

point process for representing occurrence of events larger than or equal to a

minimum threshold magnitude.

• The background events representing tectonic loading were modelled by Poisson

modelling, and the succession of aftershocks were defined by Omori-Utsu law.

ETAS MODELLING

𝑣(𝑡) = 𝐾 𝑡 + 𝑐 −𝑝

• The magnitude for tectonic and triggered events was randomly selected from a

G-R relation. The proportionality of sequence was taken proportional to K10αM,

with M magnitude and K & α as constants.

• By maximum-likelihood method; values obtained were (Hainzl et al,.2007)-

µ = 1.35 yr-1 , K = 0.0083 , α = 0.70 , p = 0.98 , c = 0.5310-5 yr. , b = 0.96 ± 0.03

1.4 APPLICATION AND RESULTS

• PGA at the site of interest was calculated

for each Ms ≥ 4.0 by using the Berge-

Thierry et al. (2003). The log(PGA) value

was randomly chosen from Gaussian

density function. Standard deviation =

0.2923, truncated at three standard

deviations.

• The solid line signifies 50 % percentile and

the dashed line signifies 90 % percentile at

Cologne. Amax value of 0.036g and 0.09g

respectively for Cologne and 0.049g and

0.12g respectively for Aachen.

IMPACT OF SHORT-TERM CLUSTERING

• Two different approaches were used for

declustering-

Case 1: following the ETAS modelling, the main shock was

described as the first event irrespective of magnitude.

Case 2: main shock was considered as the largest event in

cluster (perfect declustering).

• The effect of the time-independent hypothesis were

evaluated for the two sites through ETAS

catalogues and their corresponding Poissonian

catalogues. The figure shows the impact of

Poissonian hypothesis at the two locations.

• For every percentile the difference between the PGA values for Poissonian

catalogues and ETAS catalogues were calculated and normalized over the value

of ETAS catalogue.

• Case2, which was referred as perfect declustering, yielded an impact equal to 8 at

90% probability of non-exceedance in 50 years. This implied that with perfect

declustering the seismic hazard was being underestimated by 8 %.

IMPACT OF HISTORIC EVENTS

• Düren earthquake, February’1756 with ML = 6.4 and Roermond earthquake,

April’1992 with ML = 5.9 were selected to quantify the effect of aftershock

sequences of larger historic events using Monte Carlo simulations.

• In previous figure, top graph shows the contribution of ongoing aftershock

sequences caused by the 1992 Roermond earthquake, for the location of Cologne

and Aachen. Lower graphs show the contribution of ongoing aftershock

sequences caused by the 1756 Düren earthquake.

• Uncertainty linked to the magnitude of Düren earthquake was considered by

assuming upper and lower estimates of ML =5.9 and ML= 6.9 which were

reported as dashed and dotted lines respectively.

• Because the location of 1756 event was close to Aachen, it was found that the

contribution to the hazard was still large for high probabilities of non-

exceedance, reaching 20 % maximum for Aachen and significantly less for

Cologne. Furthermore the Roermond event,1992 contributed more than

10% to the hazard at 90% probability of non-exceedance for next 50 years.

1.5 CONCLUSIONS OF THE CASE STUDY

• Analysis showed that neglecting aftershocks led to an underestimation of the

hazard by 8 % at 90 % probability of non-exceedance in 50 years.

• Moreover, the ongoing aftershock sequence of the Roermond event, 1992

contributed 10-15% to the hazard at the level of 90% of probability of non-

exceedance. Even the Düren event, 1756 contributed about 20% to the present

hazard for the city of Aachen at the level of 95% probability of non-exceedance.

TIME-DEPENDENT PSHA OF NORTH-EAST INDIA

• Till now the probabilistic seismic hazard assessment of North-Eastern region has

been carried out in time-independent mode with declustering performed based

upon Poissonian process.

• The aim of this project is to assess the seismic hazard of North-East India

including the cluster activities to evaluate the underestimation of hazard due to

removal of aftershock events.

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